Dynamic inventory optimization method

Abstract

The invention relates to the technical field of inventory optimization, and discloses a dynamic inventory optimization method, which is used for dynamically determining an industrial optimal inventory. Through real-time data collection and preprocessing, accurate demand prediction and a dynamic replenishment strategy, an enterprise can quickly respond to market changes, and the risks of stock overstock and stockout are effectively avoided. Meanwhile, the inventory cost is further reduced through the dynamic safe inventory strategy and economic order quantity optimization calculation, and the fund utilization efficiency is improved. In addition, the method further pays attention to inventory minimization and cost optimization, and effective control of the total cost is achieved by optimizing a supply chain and formulating an inventory minimization strategy. And the accuracy of inventory data is ensured through periodic inventory and difference analysis, and the transparency and credibility of inventory management are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of inventory optimization, and in particular to a dynamic inventory optimization method. Background Art

[0002] Inventory plays a vital role in industrial production and supply chain management. It is not only the material basis for enterprise production and sales, but also an important part of enterprise capital circulation. The quality of inventory management directly affects the enterprise's operating efficiency, cost control and market competitiveness.

[0003] Inventory optimization is always a crucial link in industrial production and supply chain management. Traditional inventory management methods are often based on static data analysis and fixed inventory management strategies, which are difficult to adapt to the rapidly changing market demand and supply chain environment. Therefore, a dynamic inventory optimization method is proposed. Summary of the invention

[0004] In view of the deficiencies of the prior art, the present invention provides a dynamic inventory optimization method to solve the background technical problems.

[0005] To achieve the above object, the present invention provides the following technical solution: a dynamic inventory optimization method for dynamically determining the optimal industrial inventory, comprising the following specific steps:

[0006] (1) Data collection and preprocessing:

[0007] Establish a database that collects historical sales data, market demand trends, supply chain conditions, and supplier and distributor information data;

[0008] Perform data cleaning, including removing duplicate data, correcting outliers, and filling in missing values.

[0009] (2) Demand forecast:

[0010] Use linear regression algorithm with historical sales data to calculate and obtain specific forecast data;

[0011] Calculation formula: y = b0 + b1x, where b1 represents the slope, b0 represents the intercept, y represents the predicted sales volume, and x represents a future time point;

[0012] Select a future time point x new , substitute it into the prediction formula and calculate the predicted sales volume y new .

[0013] (3) Real-time monitoring and data analysis:

[0014] Collect inventory data, production data and sales data in real time and store them in the database;

[0015] Set inventory thresholds. When inventory levels fall below the preset thresholds, an early warning mechanism is automatically triggered.

[0016] The database has a data analysis function, which analyzes real-time inventory data, production data and sales data based on historical sales data, market demand trends, supply chain conditions, supplier and distributor information data, and displays key indicators of inventory levels, sales trends, production cycles, and inventory turnover rates.

[0017] (4) Dynamic inventory replenishment strategy:

[0018] Set the inventory trigger point s and the replenishment quantity Qi for each replenishment;

[0019] When the inventory level drops to s or below, the replenishment plan is triggered, and the replenishment quantity is Qi;

[0020] Dynamically adjust the values ​​of s and Qi based on changes in market demand, shelf life of inventory items, and seasonal demand factors;

[0021] Calculation formula:

[0022] Inventory update: I(t+1)=I(t)-D(t)+Qi, where I(t) is the inventory at time t and D(t) is the demand at time t;

[0023] Out-of-stock cost: Cs = p*max(0,sI), where p is the unit out-of-stock cost;

[0024] Purchase cost: Cb=c*Qi, where c is the unit purchase cost.

[0025] (5) Dynamic safety stock strategy:

[0026] Dynamically adjust safety stock levels based on changes in market demand and uncertainties in the supply chain;

[0027] The calculation formula of safety stock S is: S = z*√(σ2_d*L)+(Q / 2), where z is the critical value of the standard normal distribution corresponding to the service level, σ2_d is the variance of demand change, L is the delivery cycle, and Q is the economic order quantity (EOQ);

[0028] When market demand fluctuates greatly, increase the z value to increase the safety stock level.

[0029] (6) Economic Order Quantity (EOQ) optimization calculation:

[0030] According to the ordering cost S, holding cost H and annual demand D, the optimal order quantity Q is calculated using the EOQ model;

[0031] Calculation formula: Q = √((2*D*S) / H);

[0032] When ordering costs or holding costs change, recalculate the optimal order quantity.

[0033] (7) Inventory minimization and cost optimization strategies:

[0034] Reduce inventory and holding costs by optimizing the supply chain and improving the accuracy of demand forecasts;

[0035] Consider the substitutability and recyclability of inventory items and develop inventory minimization strategies;

[0036] The objective function is to minimize the total cost TC, which is calculated as follows: TC = (D / Q)*S+H*(Q / 2)+Cs+Cb+other costs;

[0037] When other costs change, recalculate the total cost and adjust the strategy.

[0038] (8) Cycle counting and variance analysis:

[0039] Conduct a comprehensive inspection and reconciliation of inventory at specific intervals;

[0040] When the inventory results are inconsistent with the records, conduct discrepancy analysis and take appropriate measures;

[0041] Variance analysis includes steps to find causes, adjust inventory records, and hold accountable.

[0042] Preferably, the method also includes supply chain collaborative optimization: obtaining inventory, demand and supply information of suppliers and distributors, and optimizing the inventory level and service level of the entire supply chain through collaborative planning.

[0043] Preferably, the method also adds a multi-factor dynamic weight allocation mechanism to dynamically adjust the weight of each factor according to market trends, seasonal changes, promotional activities and macroeconomic indicators to more accurately reflect changes in market demand.

[0044] Preferably, the dynamic inventory replenishment strategy is adjusted based on demand forecasts. Through historical sales data, market demand trends and supply chain conditions, in conjunction with the results of demand forecasts, the inventory trigger point s and each replenishment quantity Qi are adjusted to adapt to market changes.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1. Through real-time data collection and preprocessing, accurate demand forecasting and dynamic replenishment strategies, the present invention enables enterprises to respond quickly to market changes and effectively avoid inventory backlogs and out-of-stock risks. At the same time, dynamic safety stock strategies and economic order quantity optimization calculations further reduce inventory costs and improve capital utilization efficiency. In addition, the method also focuses on inventory minimization and cost optimization, and achieves effective control of total costs by optimizing the supply chain and formulating inventory minimization strategies. Cycle inventory and difference analysis ensure the accuracy of inventory data and improve the transparency and credibility of inventory management.

[0047] 2. By introducing supply chain collaborative optimization, the present invention enables enterprises to obtain inventory, demand and supply information of suppliers and distributors in real time and realize transparent information sharing. This not only helps enterprises to predict market demand more accurately, but also promotes collaborative planning among all parties in the supply chain and jointly optimizes inventory levels and service levels. This collaborative optimization reduces information asymmetry and communication barriers, improves the response speed and flexibility of the entire supply chain, and thus improves overall operational efficiency.

[0048] 3. The dynamic inventory replenishment strategy in the present invention is adjusted based on the results of demand forecasting and can adapt to market changes in real time; the enterprise can flexibly adjust the inventory trigger point s and the replenishment quantity Qi for each time according to historical sales data, market demand trends and supply chain conditions; this strategy not only helps to reduce inventory backlogs and out-of-stock risks, but also improves the market response speed of the enterprise, ensuring that the inventory level can be adjusted quickly when market demand changes, meet customer needs, and enhance market competitiveness.

[0049] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a flow chart of the dynamic inventory optimization method of the present invention;

[0051] Figure 2 This is a flow chart of the dynamic inventory replenishment strategy of the present invention;

[0052] Figure 3 The present invention is an economic order quantity (EOQ) optimization calculation flow chart;

[0053] Figure 4 This is a flow chart of the inventory minimization and cost optimization strategy of the present invention. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this technical field without creative work are within the scope of protection of the present invention.

[0055] See also Figure 1-4 , the present invention proposes a dynamic inventory optimization method, which aims to dynamically determine the optimal industrial inventory, so as to effectively manage inventory costs, improve inventory turnover and meet market demand. The specific implementation steps of the method are as follows:

[0056] (1) Data collection and preprocessing

[0057] First, establish a comprehensive database system to store various relevant data. The database must cover multi-dimensional information such as historical sales data, market demand trends, supply chain status, supplier and distributor information data, etc. Historical sales data includes but is not limited to sales time, sales volume, sales area, etc.; market demand trend data may include seasonal demand changes, changes in consumer preferences, etc.; supply chain status data includes supplier reliability, transportation time, production costs, etc.; supplier and distributor information data includes their inventory levels, order processing capabilities, etc.

[0058] After data collection is completed, data cleaning is performed to ensure data accuracy and completeness by removing duplicate data, correcting outliers (such as extreme sales data, incorrect supply chain information), and filling missing values ​​(such as using interpolation or regression models to predict missing sales data), providing a reliable basis for subsequent analysis and prediction.

[0059] (2) Demand forecast:

[0060] Use linear regression algorithms and historical sales data to calculate and obtain specific forecast data, providing a scientific basis for the formulation of inventory replenishment strategies and safety stock strategies, and reducing inventory backlogs or out-of-stock phenomena caused by inaccurate forecasts;

[0061] Practical application cases:

[0062] a) Collect historical data. Suppose we have historical sales data at n time points, recorded as (x_1, y_1), (x_2, y_2), ..., (x_n, y_n);

[0063] Among them, x_i represents a time point (such as month, quarter, etc.), and y_i represents the sales volume at that time point.

[0064] b) Calculate the regression coefficient:

[0065] The calculation formula for slope b1 is:

[0066]

[0067] in, and are the means of x and y respectively,

[0068] The calculation formula for intercept b0 is:

[0069]

[0070] c) Construct prediction formula:

[0071] Using the calculated regression coefficients b0 and b1, we can construct the prediction formula:

[0072] y=b0+b1x

[0073] in, represents the predicted sales volume, and x represents a future time point.

[0074] Suppose we have the following historical sales data: (Unit: month, sales volume)

[0075] x_1:y_100, x_2:y_120, x_3:y_130, x_4:y_140, x_5:y_150;

[0076] Calculate the mean:

[0077]

[0078]

[0079] Calculate the slope b1:

[0080] Molecular part:

[0081] Denominator: (5-3)2=4+1+0+1+4=10

[0082] Therefore, the slope b1 = -2 / 10 = -0.2

[0083] Calculate the intercept b0:

[0084] b0=132-(-0.2)*3=132+0.6=132

[0085] Construct prediction formula: y = 132.6-0.2x

[0086] Make predictions:

[0087] Suppose we want to predict the sales volume for the 6th month, that is, x new =6

[0088] y new =132.6-0.2*6=132.6-1.2=131.4

[0089] Therefore, based on this simple linear regression model, the predicted sales volume for month 6 is 131.4 units.

[0090] (3) Real-time monitoring and data analysis:

[0091] Collect inventory data, production data and sales data in real time and store them in the database. By setting inventory thresholds, when the inventory level falls below the preset threshold, the early warning mechanism is automatically triggered to remind managers to take timely measures;

[0092] The database has data analysis capabilities and can conduct in-depth analysis of real-time data based on multi-dimensional information such as historical sales data, market demand trends, and supply chain conditions. By displaying key indicators such as inventory levels, sales trends, production cycles, and inventory turnover rates, it can promptly identify inventory problems, provide decision support for managers, and improve the efficiency and accuracy of inventory management.

[0093] (4) Dynamic inventory replenishment strategy:

[0094] Set the inventory trigger point s and the replenishment quantity Qi for each replenishment; when the inventory drops to s or below, the replenishment plan is triggered, and the replenishment quantity is Qi. The specific formula used is inventory update: I(t+1)=I(t)-D(t)+Qi, where I(t) is the inventory at time t, and D(t) is the demand at time t; the replenishment strategy needs to be dynamically adjusted according to changes in market demand, the shelf life of inventory items, and seasonal demand factors.

[0095] The calculation of replenishment quantity also needs to consider out-of-stock cost and purchase cost; out-of-stock cost is determined by unit out-of-stock cost p and out-of-stock quantity, and the specific calculation formula is: out-of-stock cost: Cs=p*max(0,sI); purchase cost is determined by unit purchase cost c and replenishment quantity Qi, and the specific calculation formula is: purchase cost: Cb=c*Qi; cost minimization is achieved by optimizing replenishment strategy.

[0096] Furthermore, it also involves collaborative optimization of the supply chain; by obtaining inventory, demand and supply information from suppliers and distributors, collaborative planning is carried out to optimize the inventory level and service level of the entire supply chain; this helps to reduce uncertainty in the supply chain and improve the responsiveness and flexibility of the supply chain.

[0097] In the dynamic inventory replenishment strategy, real-time adjustments are made by combining the results of demand forecasting and information from supply chain collaborative optimization. By continuously collecting and analyzing historical sales data, market demand trends, supply chain conditions, and inventory and demand information from suppliers and distributors, the inventory trigger point s and the quantity of each replenishment Qi are adjusted. This dynamic adjustment mechanism helps to adapt to market changes, minimize costs, improve the flexibility and accuracy of inventory management, and meet market demand.

[0098] (5) Dynamic safety stock strategy:

[0099] Dynamically adjust safety stock levels based on changes in market demand and uncertainties in the supply chain (such as supplier delays, fluctuations in distributor demand, etc.);

[0100] The calculation formula of safety stock S is: S = z*√(σ2_d*L)+(Q / 2), where z is the critical value of the standard normal distribution corresponding to the service level, σ2_d is the variance of demand change, L is the delivery cycle, and Q is the economic order quantity (EOQ);

[0101] When market demand fluctuates greatly, increase the z value to improve the safety inventory level, ensure the stability of the supply chain, and reduce losses caused by out-of-stock.

[0102] (6) Economic Order Quantity (EOQ) optimization calculation:

[0103] According to the ordering cost S, holding cost H and annual demand D, the optimal order quantity Q is calculated using the EOQ model;

[0104] Calculation formula: Q = √((2*D*S) / H);

[0105] When ordering costs or holding costs change, recalculate the optimal order quantity to ensure the stability of the supply chain;

[0106] In addition, it also includes a variety of factors such as out-of-stock costs, transportation costs, and inventory depreciation costs, in order to formulate an ordering strategy that is more in line with the actual situation of the enterprise; minimize ordering costs and improve the response speed and flexibility of the supply chain.

[0107] (7) Inventory minimization and cost optimization strategies:

[0108] Reduce inventory and holding costs by optimizing the supply chain and improving the accuracy of demand forecasts;

[0109] Consider the substitutability and recyclability of inventory items and develop inventory minimization strategies;

[0110] The objective function is to minimize the total cost TC, which is calculated as follows: TC = (D / Q)*S+H*(Q / 2)+Cs+Cb+other costs;

[0111] The calculation formula takes into account a variety of factors such as ordering cost, holding cost, out-of-stock cost, and abandonment cost;

[0112] When other costs (such as out-of-stock costs and abandonment costs) change, recalculate the total cost and adjust the strategy to ensure the stability of the supply chain; reduce the total cost, improve inventory turnover, and enhance the competitiveness of the enterprise.

[0113] (8) Cycle counting and variance analysis:

[0114] The inventory is fully inspected and checked at specific time intervals; when the inventory results are inconsistent with the records, a discrepancy analysis is conducted and corresponding treatment measures are taken. The discrepancy analysis includes finding the cause, adjusting the inventory records and the steps of investigating the responsibility; in addition, quality management tools such as the PDCA cycle (Plan-Do-Check-Act) are introduced to continuously improve the problems found during the inventory process; ensure the accuracy of inventory data, find and deal with inventory problems in a timely manner, and prevent inventory losses.

[0115] This method uses a linear regression algorithm to predict demand by comprehensively collecting and preprocessing multi-dimensional data including historical sales data, market demand trends, supply chain conditions, and supplier and distributor information. Subsequently, it monitors inventory, production, and sales data in real time, sets an inventory threshold warning mechanism, and provides data analysis support. On this basis, it implements a dynamic inventory replenishment strategy, adjusts replenishment quantities and trigger points in combination with demand forecasts and supply chain collaborative optimization information, and adopts a dynamic safety stock strategy and economic order quantity (EOQ) optimization calculation to ensure that inventory costs are minimized and service levels are optimized. In addition, inventory minimization and cost optimization strategies, as well as cycle counting and variance analysis, are considered, and quality management tools are used to continuously improve inventory management. This series of processes effectively improves inventory turnover, reduces inventory costs, and flexibly meets market demand, achieving comprehensive optimization of inventory management.

[0116] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A dynamic inventory optimization method for dynamically determining the optimal industrial inventory, characterized in that: The specific steps include: (1) Data collection and preprocessing: Establish a database that collects historical sales data, market demand trends, supply chain conditions, and supplier and distributor information data; Perform data cleaning, including removing duplicate data, correcting outliers, and filling in missing values. (2) Demand forecast: Use linear regression algorithm with historical sales data to calculate and obtain specific forecast data; Calculation formula: y = b0 + b1x, where b1 represents the slope, b0 represents the intercept, y represents the predicted sales volume, and x represents a future time point; Select a future time point x new , substitute it into the forecast formula and calculate the predicted sales volume x new . (3) Real-time monitoring and data analysis: Collect inventory data, production data and sales data in real time and store them in the database; Set inventory thresholds. When inventory levels fall below the preset thresholds, an early warning mechanism is automatically triggered. The database has a data analysis function, which analyzes real-time inventory data, production data and sales data based on historical sales data, market demand trends, supply chain conditions, supplier and distributor information data, and displays key indicators of inventory levels, sales trends, production cycles, and inventory turnover rates. (4) Dynamic inventory replenishment strategy: Set the inventory trigger point s and the replenishment quantity Qi for each replenishment; When the inventory level drops to s or below, the replenishment plan is triggered, and the replenishment quantity is Qi; Dynamically adjust the values ​​of s and Qi based on changes in market demand, shelf life of inventory items, and seasonal demand factors; Calculation formula: Inventory update: I(t+1)=I(t)-D(t)+Qi, where I(t) is the inventory at time t and D(t) is the demand at time t; Out-of-stock cost: Cs = p*max(0,sI), where p is the unit out-of-stock cost; Purchase cost: Cb=c*Qi, where c is the unit purchase cost. (5) Dynamic safety stock strategy: Dynamically adjust safety stock levels based on changes in market demand and uncertainties in the supply chain; The calculation formula of safety stock S is: S = z*√(σ2_d*L)+(Q / 2), where z is the critical value of the standard normal distribution corresponding to the service level, σ2_d is the variance of demand change, L is the delivery cycle, and Q is the economic order quantity (EOQ); When market demand fluctuates greatly, increase the z value to increase the safety stock level. (6) Economic Order Quantity (EOQ) optimization calculation: According to the ordering cost S, holding cost H and annual demand D, the optimal order quantity Q is calculated using the EOQ model; Calculation formula: Q = √((2*D*S) / H); When ordering costs or holding costs change, recalculate the optimal order quantity. (7) Inventory minimization and cost optimization strategies: Reduce inventory and holding costs by optimizing the supply chain and improving the accuracy of demand forecasts; Consider the substitutability and recyclability of inventory items and develop inventory minimization strategies; The objective function is to minimize the total cost TC, which is calculated as follows: TC = (D / Q)*S+H*(Q / 2)+Cs+Cb+other costs; When other costs change, recalculate the total cost and adjust the strategy. (8) Cycle counting and variance analysis: Conduct a comprehensive inspection and reconciliation of inventory at specific intervals; When the inventory results are inconsistent with the records, conduct discrepancy analysis and take appropriate measures; Variance analysis includes steps to find causes, adjust inventory records, and assign responsibilities.

2. The comprehensive dynamic inventory optimization method and system according to claim 1, characterized in that: The approach also includes supply chain collaborative optimization: obtaining inventory, demand, and supply information from suppliers and distributors, and optimizing inventory and service levels across the supply chain through collaborative planning.

3. The comprehensive dynamic inventory optimization method and system according to claim 1, characterized in that: This method also adds a multi-factor dynamic weight allocation mechanism, which dynamically adjusts the weight of each factor based on market trends, seasonal changes, promotional activities and macroeconomic indicators to more accurately reflect changes in market demand.

4. A dynamic inventory optimization method according to claim 1, characterized in that: The dynamic inventory replenishment strategy is adjusted based on demand forecasts. Through historical sales data, market demand trends and supply chain conditions, combined with the results of demand forecasts, the inventory trigger point s and the replenishment quantity Qi are adjusted to adapt to market changes.

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